Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1551–1560 of 6661 papers

Title	Date	Tasks	Status	Hype
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking	Aug 24, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
Self-Supervised Graph Co-Training for Session-based Recommendation	Aug 24, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection	Aug 23, 2021	Anomaly DetectionAttribute	CodeCode Available	1
Unsupervised Local Discrimination for Medical Images	Aug 21, 2021	Contrastive LearningLesion Segmentation	CodeCode Available	1
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models	Aug 19, 2021	Contrastive LearningDecoder	CodeCode Available	1
Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization	Aug 19, 2021	Contrastive LearningDomain Generalization	CodeCode Available	1
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection	Aug 17, 2021	2D Object Detection3D Object Detection	CodeCode Available	1
Graph Contrastive Learning for Anomaly Detection	Aug 17, 2021	Anomaly DetectionBinary Classification	CodeCode Available	1
Scene Designer: a Unified Model for Scene Search and Synthesis from Sketch	Aug 16, 2021	Contrastive LearningGraph Neural Network	CodeCode Available	1
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation	Aug 14, 2021	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified